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\section{Conclusion}
\label{sec:conclusion}

We develop two models of prediction using the entire training data set of KDDCup 98.
We make a logistic regression model using Fast Large Margin with logistic regression kernel for predicting
the probability of TARGET\_B =1.
We make a linear regression model using the standard operator in RapidMiner in order to predict conditional 
donation amounts in dollars or TARGET\_D for the responding donors.

We then apply the two models on the test data set of KDDCup 98 and obtain the two predictions of TARGET\_B and TARGET\_D. 
We then take a product of these two predictions and find if the the result is greater than $0.68$. The resulting subset of \optsol{} donors
who satisfied this criteria are the ones whom we advise to be solicited by the organization. If solicited the total profit
that the organization stands to gain is \optprofit{}.
We use the test data set only once after learning the two training models.

 

 
